Capabilities Of Machine Tools Research Articles

Holistic evaluation of involute gear surfaces using 3D point cloud inversion

Gear users desire increased performance from their products and are interested in predicting the behavior (vibration, loss, service life, etc), and gear manufacturers are interested in adjusting machining parameters to manufacture even better gears. Both require proficient metrology. The evaluation standards were originally written to classify the manufacturing capability of machine tools and as tolerances tighten they may not provide enough insight into the functional performance of the manufactured parts. Furthermore, the advantage of modern coordinate and gear measuring machines is not fully taken into account when applying standardized measurement and evaluation strategies. Improved analysis tools can increase the relevant information gained from current measurement capabilities and enable more useful functional characteristics to be specified by designers and manufacturers. A key improvement is to move from profile and helix line traces to flank surfaces and corresponding fitted form elements. This is achieved by the introduced method. We present an inversion algorithm that fits dimension, form, and pose, and is applicable with current methods through freezing of specific fitting parameters. The method fits a synthetic point cloud with output results that are accurate to the 7th digit, or in practical terms are numerically equal. The inversion method can increase the understanding of error contributions and the functional performance of gears.

FPGA-Based Optimization of Industrial Numerical Machine Tool Servo Drives

This paper presents an analysis of the advantages stemming from the application of field-programmable gate arrays (FPGAs) in servo drives used within the control systems of industrial numerical machine tools. The method of improving the control system that allows for increasing the precision of machining, as well as incorporating new functionalities and streamlining diagnostic processes, is described. As demonstrated, the utilization of digital controllers with robust computational power and high-performance real-time communication interfaces is essential for achieving these objectives. This study underscores the limitations of commonly employed digital controllers in servo drives, which are constructed based on microcontrollers or signal processors collaborating with application-specific integrated circuits (ASICs). In contrast, the proposed FPGA-based solution offers substantial computational power and significantly reduced latencies in the real-time communication interface compared to other examined alternatives. This enables the realization of the planned objectives, specifically the enhancement of technical parameters and diagnostic capabilities of machine tools. Furthermore, the research indicates that FPGA-based digital controllers exhibit relatively low power consumption and a simplified design of the electronic printed circuit board in comparison to other analyzed digital platforms. These features can contribute to heightened reliability and diminished production costs of such controllers. Additional conclusions drawn from the study indicate that FPGA-based controllers provide greater developmental possibilities and their production is marked by potential resilience to challenges associated with the availability of electronic components in the market.

Classification of materials in cylindrical workpieces using image processing and machine learning techniques

Smart machine tools must be able to recognise the materials they interact with, in order to independently decide on what actions to be taken whenever needed. The purpose of this research is to offer a generic method to automate material identification and classification task utilising image processing and machine learning techniques so as to improve the cognitive capabilities of machine tools. A dataset of four material surfaces; Copper, Aluminium, Stainless Steel, and Bronze is generated and the RGB data are extracted. These colour channels are utilised as input features to train the machine learning algorithms. Convolutional Neural Network (CNN) and other classification methods like Support Vector Machine (SVM), Decision Trees, and k-Nearest Neighbour are also used to classify material images based on the generated dataset. A unique generalised technique, based on CNN, has been proposed to accurately recognise and categorise round surfaced materials during machining. The accuracy achieved by the classifier has reached 100% during training and testing. Test images are used to confirm the proposed methodology's capability to distinguish between materials based on various illumination environments and camera positions.

Multi-objective parameter optimization of CNC plane milling for sustainable manufacturing.

Energy modeling and cutting parameter optimization of the machining process have been recognized as powerful and effective ways to save energy. However, in the actual machining process, technologists often use empirical methods to determine the final cutting parameters. Due to the lack of theoretical support and optimization tools, this method is difficult to fully consider the constraints of machine tool capability, cutting tool performance, and workpiece material, which affects the overall performance of the machine tool to give full play. To address this problem, a multi-objective parameter optimization method of computer numerical control (CNC) plane milling for sustainable manufacturing was proposed in this paper. More specifically, three tasks were carried out: (1) an accurate milling energy model considering transient processes such as spindle acceleration was established; (2) a multi-objective parameter optimization model of CNC plane milling was established with cutting parameters as optimization variables and considering various complex constraints; (3) by drawing 3D surface maps, the internal relationship between the cutting parameters and the optimization index was presented in detail and intuitively. Finally, a case study was carried out in the XHK-714F vertical machining center. The results showed that the processing efficiency is improved by 21.0%, the energy consumption is reduced by 15.3%, and the surface roughness is reduced by 5.5% through the optimization of cutting parameters, which verified the effectiveness and feasibility of the proposed model and method.

Digital Model and Assembling of a Lathe

The article aims at developing a digital model of a lathe and the related technology for its assembling. The study is based on analyzing the service purpose and technological capabilities of modern modular machine tools, justification and development of the machine tool design according to the specified production conditions, and development of a technological process for assembling the proposed modular machine tool. The geometric modeling techniques and the design documentation were implemented to justify the rational choice of design parameters of the machine tool design and its spatial model. The proposed approach also considers structural elements and the relationships between them. As a result, a conceptual approach was proposed to design technological processes of lathe assembly with a wide range of technological capabilities. It allows implementation of the up-to-date strategy from idea to finished product at industrial enterprises. The practical significance of the obtained results for the machine-building industry is in the proposed practical recommendations for developing the technological process for assembling lathes.

An ontology self-learning approach for CNC machine capability information integration and representation in cloud manufacturing

Manufacturing activities in cloud-based environments strongly rely on the online integration and description of the capability of machining resources. In the authors’ previous work, STEP-NC schema was applied to construct an ontology model to support the integration and reasoning of machine tool information and capability. For the maintenance and update of the model, in this paper, a self-learning method is proposed to explore correlations from STEP-NC process planning documents to obtain machining knowledge to improve the comprehensiveness of the model. In this method, a Map/Reduce-based Apriori algorithm is developed incombination with the built ontological model. First, a dataset is extracted from the document according to the importance analysis results of the model. Then, a mining procedure that combinesApriori algorithm and Map/Reduce framework is developed. Finally, two representation modes are adopted to embed the mined results into the model. According to the outcomes of a preliminary experiment with standard STEP-NC documents, this method effectively enables the ontology learning mechanism from the aspects of time consumption and mined associations, which improves the suitability of the enriched ontology model to handle information integration and industrial applications.

On-Machine Measurement of Wheel Tread Profile With the 1-D Laser Sensor

Wheel is an important component of bogie, and its geometry affects the dynamic behaviors of rail vehicles. To accurately obtain the geometric feature information of wheel tread profile, an on-machine measurement method with the 1-D laser sensor is investigated in this paper. Combined with the specific morphology of tread profile and measurement model of laser sensor, the corresponding on-machine measurement model of wheel tread profile is established according to the characteristics of inclined and curved profiles. To improve the measurement accuracy, an adaptive error correction is studied to strengthen the measurement of complex profiles and suppress the measurement error of laser sensors. And the method achieves error suppression by more than 30% in the test and calibration experiments. Finally, the measurement method based on the laser sensor is put into the practical measurement of the wheel tread profile on an underfloor wheelset lathe, and the effectiveness of proposed method is verified with experiments. These works can not only improve the measurement accuracy of wheel geometry but also enhance the machining capability of machine tool, which is important for the safe operation of rail vehicles.

Validation of a Coupled Simulation for Machine Tool Dynamics Using a Linear Drive Actuator

In order to ensure high productivity capabilities of machine tools at a low cost but at increased geometric accuracy, modeling of their static and dynamic behavior is a crucial task in structure optimization. The drive control and the frictional forces acting in feed axes significantly determine the machine’s response in the frequency domain. The aim of this study was the accurate modeling and the experimental investigation of dynamic damping effects using a machine tool test rig with three-axis kinematics. For this purpose, an order-reduced finite element model of the mechanical structure was coupled with models of the drive control and of the non-linear friction behavior. In order to validate the individual models, a new actuator system based on a tubular linear drive was used for frequency response measurements during uniaxial carriage movements. A comparison of the dynamic measurements with the simulation results revealed a good match of amplitudes in the frequency domain by considering dynamic damping. Accordingly, the overall dynamic behavior of machine tool structures can be predicted and thus optimized by a coupled simulation at higher level of detail and by considering the damping effects of friction. Dynamic testing with the newly designed actuator is a prerequisite for model validation and control drive parameterization.

Vibration stability criterion in assessing the dynamic quality and limiting capabilities of machine tools

The vibration resistance of the dynamic system of a cutting machine is considered as a criterion for assessing its dynamic quality and limiting capabilities. An example of diagnosing a group of machines of the same type by the criterion of vibration resistance with an assessment of their current state is given.

Improving the Performance of Multipurpose Machine Tools

Attention focuses on means of improving the efficiency and capabilities of multipurpose machine tools by expanding the range of available tools and the methods of surface machining. As an example, new approaches are applied to the manufacture of complex parts by means of a multipurpose machine tool. The machining efficiency is improved.

Self-Configuration Machining capability of a 4-DOF parallel kinematic machine tool with nonsingular workspace for Intelligent Manufacturing Systems

Abstract Machining accuracy is affected by varying kinematic properties and singularities of parallel mechanisms inside their workspace. In this research an asymmetrical four degree of freedom parallel kinematic machine tool with rotational capability around one of its horizontal axis, developed for milling processes is investigated for its machining capability. Surface roughness of Al5083 alloy is analysed after machining on various domains of available workspace. Linear interpolation was used in creating tool paths to avoid kinematic non linearity of parallel mechanisms and experiments are conducted using Response Surface Methodology (RSM) and gathered data is modelled by linear regression. Surface roughness results demonstrated the high machining performance of the mechanism inside available workspace.

What’s Going on Inside?

Machine tools today draw on globally available components in bearings, linear ways, motors and CNC units. Machinery has cast around to see what recent developments have been made, because they will be reflected in future machine tool capability and performance

Through Life Machine Tool Capability Modelling

Through-life analysis of the machine tool capability is becoming increasingly important as there now exist a plethora of machine tool types, different machine tool testing and verification standards, a variety of machine tool testing equipment and proprietary maintenance procedures. The need to represent the actual manufacturing capability of equipment is a challenge for manufacturing industry as accessing this through-life information remains a major bottleneck. Though machines can be modelled at various levels of fidelity from simple computerized datasheets containing overall machining dimensions/power and positional capability to complex computer aided models of machine tools, there exists a gap to represent their operational health throughout the machine life to consider capability degradation. This paper outlines a new information model which enables through-life machine tool capability to be represented for establishing a digital twin, improved process planning and machine selection for parts as well as preventative maintenance scheduling.

Modelling and Simulation of a New Variable Stiffness Holder for Milling of Flexible Details

Abstract Modern industry expectations in terms of milling operations often demand the milling of the flexible details by using slender ball-end tools. This is a difficult task because of possible vibration occurrence. Due to existence of certain conditions (small depths of cutting, regeneration phenomena), cutting process may become unstable and self-excited chatter vibration may appear. Frequency of the chatter vibration is close to dominant natural frequency of the workpiece or the tool. One of the methods of chatter vibration avoidance is matching the spindle speed to the optimum phase shift between subsequent cutting edges passes (i.e. the Liao-Young condition). However, the set of optimum spindle speeds from the point of view of vibration reduction may be not optimum one from other points of view. For example milling efficiency or machine tool capabilities cannot be assured. This article presents the idea of a workpiece holder with adjustable stiffness and discusses a new variant of its realization. In the holder, milling process is performed at constant spindle speed and feed speed. In order to avoid vibration the holder stiffness is modified. Stiffness changes modify natural frequencies of the workpiece and thus it is possible to modify dynamic properties of the workpiece in such a way that arbitrary chosen, constant spindle speed will be optimum, due to the Liao-Young condition performance. Calculation of the optimum stiffness is performed before milling, based on the workpieces modal identification results and the finite element model simulations.

An autonomous multisensor in situ metrology system for enabling high dynamic range measurement of 3D surfaces on precision machine tools

An in situ measurement is of prime importance when trying to maintain the position of the workpiece for further compensation processes in order to improve the accuracy and efficiency of the precision machining of three dimensional (3D) surfaces. However, the coordinates of most of the machine tools with closed machine interfaces and control system are not accessible for users, which make it difficult to use the motion axes of the machine tool for in situ measurements. This paper presents an autonomous multisensor in situ metrology system for enabling high dynamic range measurement of 3D surfaces on precision machine tools. It makes use of a designed tool path and an additional motion sensor to assist the registration of time-space data for the position estimation of a 2D laser scanner which measures the surface with a high lateral resolution and large area without the need to interface with the machine tool system. A prototype system was built and integrated into an ultra-precision polishing machine. Experimental results show that it measures the 3D surfaces with high resolution, high repeatability, and large measurement range. The system not only improves the efficiency and accuracy of the precision machining process but also extends the capability of machine tools.

Cloud-based adaptive process planning considering availability and capabilities of machine tools

Disturbances on manufacturing shop-floors and the increasing number of product variants necessitate adaptive and flexible process planning methods. This paper proposes a service-oriented Cloud-based software framework comprising two services. The first service generates non-linear process plans using event-driven function blocks and a genetic algorithm. The second service, gathers data from shop-floor machine tools through sensors, input from operators, and machine schedules. An information fusion technique processes the monitoring data in order to feed the process planning service with the status, specifications, and availability time windows of machine tools. The methodology is validated in a case study of a machining SME.

Kinematic analysis and feedrate optimization in six-axis NC abrasive belt grinding of blades

The purpose of this paper is to propose a kinematic analysis and optimization method for a kind of six-axis numerical control (NC) belt grinding machine tools when grinding blades. Six-axis NC abrasive belt grinding machine tools have been widely used for grinding blades in the aero engine manufacturing enterprises. Kinematic analysis and feedrate optimization play an important role in efficient machining with NC machine tools. After analyzing the basic kinematic relation of the six-axis NC abrasive belt grinding machine tool, coordinate systems are established to calculate the generalized kinematics model. The formulas of the rotary angles and the motion coordinates are provided by simultaneously transforming the three vectors which control the motions of the abrasive belt contact disk. An efficient strategy for feedrate optimization is developed to meet the accuracy requirements of blade abrasive belt grinding process. Both the load capacity of process system and the servo drive capability of machine tools are considered. The validity is demonstrated with simulations and experiments on a TX6-1000HV computer numerical control (CNC) machine tool.

Convex optimisation method for time-optimal feedrate planning with complex constraints

Time-optimal feedrate planning theory has been widely applied in high-speed machining to improve machining efficiencies with a specified trajectory accuracy by fully exploiting the capabilities of high-speed computer numerical control machine tools. Reasonable and effective constraints conforming to practical situations are necessary conditions for obtaining optimal planning results. In this article, complex constraints, such as the motor torque constraint, the contour error constraint, the friction error constraint, the instruction velocity constraint and the smooth velocity constraint, are investigated in depth. Moreover, an efficient convex optimisation method is developed to address the time-optimal feedrate planning problem with these complex constraints. The experimental results from two feedrate planning tests and the computational efficiency for a test trajectory demonstrate the validity, stability and high efficiency of the proposed approach.

Feature Based Machine Tool Accuracy Analysis Method

Machine tool accuracy is the most important performance parameters which affect the part quality. At present, a systematic machine tool accuracy evaluation method is necessary for the machine tool selection in process planning and shop-floor scheduling. This paper proposes an efficient feature based machine tool accuracy analysis method to enable machine tool capability evaluation about accuracy, and the mapping from the machine tool accuracy to the part feature tolerance is established in this method. The cutter is used as a bridge to transform the machine tool error to feature tolerance. The deviation of the cutter between the actual position & orientation and the nominal position & orientation is converted from the machine tool error according to the rigid body kinematics method. Then the feature error in the form of GD&T is calculated from the profile of the feature and the deviation of the cutter. A prototype system has been developed based on this research. An industrial case study shows that the methodology is effective.

Machine tool capability profiles for representing machine tool health

Control charts at present are used to provide statistical representation of machine tool health. These charts are based on machine tool testing standards, for example ISO 230, ASME B5.54 and VDI 3441, and can be utilised to decide a particular resource?s level of utilisation during manufacturing execution. Although these standards provide an indication of machine tool accuracy, they do not provide any mechanism to exchange and use health information within these control charts to identify whether a particular machine is performing to the desired level of capability and whether that machine is healthy. It is only after control charts are manually interpreted, that machine tool selection decisions can be made. This paper reports research that exploits and extends an ISO 14649 (STEP-NC) Part 201 and Part 200 series (Machine Tool Data Model) for representing machine tool health data. This provides a new approach for representing statistical machine tool accuracy information while maintaining the compliancy within prevalent machine tool testing standards. A data model for representing machine tool health based on capability profiles has been proposed. A case study of machine tool showing the interpretation of a control chart with proposed data model has been utilised to represent machine tool health through capability profiles. A data model for exchanging machine tool health information is proposed.Manufacturing resource capability profiles can represents state of the machine tool.Machine tool health data is stored and exchanged with ISO 10303 part-21 file format.Machine tool technological specifications are connected to the health information.Standardised machine tool health information offer interoperability advantages.